Principal component analysis in application to Brillouin microscopy data

Mahmodi, H; Poulton, CG; Leslie, MN; Oldham, G; Ong, HX; Langford, SJ; Kabakova, IV

Principal component analysis in application to Brillouin microscopy data

Mahmodi, H Poulton, CG Leslie, MN Oldham, G Ong, HX Langford, SJ Kabakova, IV

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Publisher:: IOP Publishing Ltd
Publication Type:: Journal Article
Citation:: JPhys Photonics, 2024, 6, (2)
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Mahmodi, H
dc.contributor.author	Poulton, CG
dc.contributor.author	Leslie, MN
dc.contributor.author	Oldham, G
dc.contributor.author	Ong, HX
dc.contributor.author	Langford, SJ
dc.contributor.author	Kabakova, IV
dc.date.accessioned	2024-12-05T16:07:35Z
dc.date.available	2024-12-05T16:07:35Z
dc.date.issued	2024-04-01
dc.identifier.citation	JPhys Photonics, 2024, 6, (2)
dc.identifier.issn	2515-7647
dc.identifier.issn	2515-7647
dc.identifier.uri	http://hdl.handle.net/10453/182370
dc.description.abstract	Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the microscale mechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment represents the high-dimensional set of spectral information, i.e. each pixel within a 2D/3D Brillouin image is associated with hundreds of points of spectral data. Its analysis requires non-trivial approaches due to subtlety in spectral variations as well as spatial and spectral overlaps of measured features. This article offers a guide to the application of Principal Component Analysis (PCA) for processing Brillouin imaging data. Being unsupervised multivariate analysis, PCA is well-suited to tackle processing of complex Brillouin spectra from heterogeneous biological samples with minimal a priori information requirements. We point out the importance of data pre-processing steps in order to improve outcomes of PCA. We also present a strategy where PCA combined with k-means clustering method can provide a working solution to data reconstruction and deeper insights into sample composition, structure and mechanics.
dc.language	English
dc.publisher	IOP Publishing Ltd
dc.relation	http://purl.org/au-research/grants/arc/CE230100021
dc.relation	http://purl.org/au-research/grants/arc/CE230100006
dc.relation	http://purl.org/au-research/grants/arc/DP200101893
dc.relation.ispartof	JPhys Photonics
dc.relation.isbasedon	10.1088/2515-7647/ad369d
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	5102 Atomic, molecular and optical physics
dc.subject.classification	5108 Quantum physics
dc.title	Principal component analysis in application to Brillouin microscopy data
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Health Technologies (CHT)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Institute of Biomedical Materials and Devices (IBMD)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Clean Energy Technology (CCET)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Institute of Biomedical Materials and Devices (IBMD)/Institute of Biomedical Materials and Devices (IBMD) Associate Members
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-12-05T16:07:30Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	2

Abstract:

Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the microscale mechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment represents the high-dimensional set of spectral information, i.e. each pixel within a 2D/3D Brillouin image is associated with hundreds of points of spectral data. Its analysis requires non-trivial approaches due to subtlety in spectral variations as well as spatial and spectral overlaps of measured features. This article offers a guide to the application of Principal Component Analysis (PCA) for processing Brillouin imaging data. Being unsupervised multivariate analysis, PCA is well-suited to tackle processing of complex Brillouin spectra from heterogeneous biological samples with minimal a priori information requirements. We point out the importance of data pre-processing steps in order to improve outcomes of PCA. We also present a strategy where PCA combined with k-means clustering method can provide a working solution to data reconstruction and deeper insights into sample composition, structure and mechanics.

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http://hdl.handle.net/10453/182370